In times of health crises disease situation awareness is critical in the prevention and containment of the disease. One indicator for the development of many contagious diseases is the presence of fever and the proposed system, IRFIS, extends prior research into fever detection via infrared imaging in two key ways. Firstly, the system utilizes a modern, machine learning based object detection model for detecting heads, supplanting the traditional methods that relied upon shape matching. Secondly, IRFIS is capable of running from the Android mobile platform using a small, commercial-grade infrared camera. IRFIS’s head detection model when evaluated on a dataset of unseen images, achieved an AP of 96.7% with an IoU of 0.50 and an AR of 75.7% averaged over IoU values between 0.50 and 0.95. IRFIS calculates the target’s maximum temperature in the detected head sub-image and real results are presented as well as avenues of future work are explored.
Responding to health crises requires the deployment of accurate and timely situation awareness. Understanding the location of geographical risk factors could assist in preventing the spread of contagious diseases and the system developed, Covid ID, is an attempt to solve this problem through the crowd sourcing of machine learning sensor-based health related detection reports. Specifically, Covid ID uses mobile-based Computer Vision and Machine Learning with a multi-faceted approach to understanding potential risks related to Mask Detection, Crowd Density Estimation, Social Distancing Analysis, and IR Fever Detection. Both visible-spectrum and LWIR images are used. Real results for all modules are presented along with the developed Android Application and supporting backend.
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